The statements in this section merely provide background information related to the present disclosure and may not constitute prior art.
Generally, wavelet transform-based video data compression technique provides a solution to the blocking artifacts caused by conventional JPEG or MPEG-x, H.26L and other block-centered data processing methods, and is anticipated to be an excellent technique to provide scalability and progressive transmission adapting to the transmission and storage medium atmosphere as it is being applied to recent international standard JPEG2000 and Dirac, which is a video compression technique developed by British BBC.
Recent trends of discrete wavelet transform technique are directed in two ways: the first of which is towards using video signal's intrinsic characters of the directional components of its line, edge, and outline to improve the coding gain; and the second is to improve the coding gain by changing the filter tap following the edge of the video signal. Typical methods using the video signal directional component are next-generation discrete wavelet transform techniques such as Contourlet, Directionlet, and DADWT, which perform filtering along the image contour or edge directions and provide high vanishing moments, obtaining high coding gain.
Methods of varying filter taps along the video signal edge are implemented with [Reference 1] space-adaptive transform, [Reference 2] spatially adaptive wavelet video coding, and [Reference 3] MINT (Median based minimum variance interpolation). Such conventional standard wavelet transform techniques have provided high vanishing moments with respect to relatively uniform signals, although they have limitations of generating high wavelet coefficients against singularities such as edge or outline and there were methods suggested to handle the edge or outline problems by adaptively changing the filter taps in an effort to improve the coding gain.    [Reference 1] Roger L. Claypoole, Geoffrey M. Davis, Wim Sweldens, and Richard G. Baraniuk (“Nonlinear Wavelet Transforms for Image Coding via Lifting”, IEEE Trans. Image Processing, vol. 12, no. 12, pp. 14491459, December 2003)    [Reference 2] Zhen Li, Feng Fu, Shipeng Li, and Edward Delp (“Wavelet Video Coding Via a Spatially Adaptive Lifting Structure”, IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, vol. 3, pp. 9396, 2003)    [Reference 3] Nikolaos V. Boulgouris, Dimitrios Tzvaras, Michael G. Strintzis (“Lossless Image Compression Based on Optimal Prediction, Adaptive Lifting, and Conditional Arithmetic Coding”, IEEE Trans. Image Processing, vol. 10, no. 1, January 2001)
More specifically, [Reference 1] incorporates a 3×3 2-dimensional predicted window to find the edge location or starting point in the corresponding window. If there exists an edge, it is projected to truncated Fourier base to generate an edge model. Through the obtained edge model, the filter tap length of a prediction filter is determined as in FIG. 1. In FIG. 1 covering an inter-pixel interface B (so called to represent a steep pixel value change along a same row of pixels), dark circles are updated pixels xe[n] and bright circles are pixels to be predicted xo[n]. The process of predicting the bright circles are comprised of their adjacent dark circles combined, and the number to a bright circle represent the number of dark circles used to predict the bright circle that equals to the filter tap length of the prediction filter. The [Reference 1] space-adaptive transform technique simply changes the filter tap depending on the existence of the edge without taking the edge shape or strength into account.
[Reference 3] adaptively selects filters by using a median hybrid filter which selects one with median residual signal among the optimal linear filters for reflecting local video characteristics.
Although the above described conventional methods have brought about improvements in the subjective image quality of mitigating the ringing artifacts and maintaining the edge sharpness through applying a long-tap filter to smooth areas of a video and a short-tap filter to the edge, they are recognized as having limitations in improving the images in the sense of objective quality. Because the wavelet generates smaller wavelet coefficients for a smooth video signal as the vanishing moment of the wavelet filter gets greater, the short-tap filters with a smaller vanishing moment is restricted to generate a relatively greater wavelet coefficient for the smooth video signal. In other words, using the short-tap filter in an insignificant gradient of the video signal edge or using an excessive number of the short-tap filters causes a limitation on the coding gain.
However, there are instances where the long-tap filter can predict signals better than the short-tap filter even at the presence of the edge. For example, let us assume there is an edge B between pixel x[2n+1] to be predicted at present and a left side pixel c[n] of the pixel x[2n+1] as shown in FIG. 2. c[n2], c[n1], c[n], c[n+1], and c[n+2] are pixels updated by using their adjacent pixels. In this event, since the described conventional methods determine the filter length depending on the presence of the edge, the short-tap filter would be selected and the prediction of c[n+2] is supposed to performed by using c[n]. In this event, because the prediction of x[2n+1] is done by using information across the edge, having little relevance to actual information to be predicted, the prediction accuracy becomes way too low, causing a large wavelet coefficient. Therefore, this situation may be better solved by using a long-tap filter rather than the short-tap filter to predict pixel x[2n+1] from appropriate pixels containing information similar to pixel x[2n+1]. However, those prior art methods relying uniformly on the presence or absence of the edge in determining the filter tap length entail the prediction inaccuracy and the low coding efficiency.